Data Exploration Gabriel Hamulic

Dataset: US Real Estate Dataset

Instructions

Your report must be of high quality, meaning that your report:

• is visually and textually pleasing of
• does not look/read/feel like a draft instead of a finished analysis
• explains/discusses your findings and results in the main text, e.g., explain/discuss all figures/table in the main text
• is representable such that it can show to any interested third party
• uses figure/table captions/linking/reference (see example further down)
• Do not show any standard printout of R-code, use for data.frame/tibbles knitr::kable() printing.
• Do not simply print datasets (too many lines) use instead rmarkdown::paged_table()

Introduction

In this project, we explore a large dataset of over 2 million real estate listings across the United States. The data includes key property attributes such as price, size, lot area, location, and listing status.

Our goal is to gain a deeper understanding of the current U.S. housing market by identifying:

• Patterns in property prices and sizes across states and cities

• Relationships between housing characteristics (e.g., size, bedrooms, bathrooms)

• Regional differences in affordability and market activity

Libraries

library <- function(...) {suppressPackageStartupMessages(base::library(...))}
library(tidyverse)
library(dplyr)
library(knitr)
library(tidyr)
library(rmarkdown)
library(janitor)
library(scales)
library(tidytext)
library(ggforce)
library(GGally)
library(DT)
library(kableExtra)
library(broom)
library(plotly)

Data

Data source

In this data exploration we are looking at the US Real Estate market with use of a dataset from kaggle published by Ahmed Shahriar Sakib. It contains over 2.2 Million Real Estate listings broken down to State, Size, Price (among other factors). (Source: https://www.kaggle.com/datasets/ahmedshahriarsakib/usa-real-estate-dataset/data)

Data import

data = read.csv("data/realtor-data.zip.csv") # Data import

Data Transformation

data = subset(data, select = c(status, price, bed, bath, acre_lot, city, state, house_size)) # keep relevant columns

Structure with standard datatypes

structure_tbl <- tibble::tibble(
  Variable = names(data),
  Type = sapply(data, function(x) class(x)[1]),
  Example = sapply(data, function(x) {
    val <- unique(x[!is.na(x)])[1]
    if (is.factor(val)) 
      as.character(val)
  }),
  Missing = sapply(data, function(x) sum(is.na(x)))
)

kable(
  structure_tbl,
  caption = "Structure summary of the dataset",
  align = c("l", "l", "l", "r")
)

Structure of cleaned dataset

Variable	Type	Example	Missing
status	character	NULL	0
price	numeric	NULL	1541
bed	integer	NULL	481317
bath	integer	NULL	511771
acre_lot	numeric	NULL	325589
city	character	NULL	0
state	character	NULL	0
house_size	numeric	NULL	568484

# Assign Data Types
data$status = as.factor(data$status)
data$city = as.factor(data$city)
data$state = as.factor(data$state)

NA Removal

before_rows <- nrow(data)
data <- na.omit(data)
after_rows <- nrow(data)
kable(data.frame(Description = c("Before NA removal", "After NA removal"),
Rows = c(before_rows, after_rows)))

Description	Rows
Before NA removal	2226382
After NA removal	1360716

The dataset now has 1360716 observations and 8 variables after removing rows with missing values.

Filtering

# Filter min and max values
data = data |>
  filter(price > 10000 & price < 1000000000)

Calculations

data = data |>
  mutate(price_per_sqm = price/house_size)

Cleaned Dataset

paged_table(data)

Structure after transformation

structure_tbl <- tibble::tibble(
  Variable = names(data),
  Type = sapply(data, function(x) class(x)[1]),
  Example = sapply(data, function(x) {
    val <- unique(x[!is.na(x)])[1]
    if (is.factor(val)) as.character(val) else as.character(round(val, 2))
  }),
  Missing = sapply(data, function(x) sum(is.na(x)))
)

kable(
  structure_tbl,
  caption = "Structure summary of the dataset",
  align = c("l", "l", "l", "r")
)

Structure of cleaned dataset

Variable	Type	Example	Missing
status	factor	for_sale	0
price	numeric	105000	0
bed	integer	3	0
bath	integer	2	0
acre_lot	numeric	0.12	0
city	factor	Adjuntas	0
state	factor	Puerto Rico	0
house_size	numeric	920	0
price_per_sqm	numeric	114.13	0

After cleaning the dataset, all variables have appropriate data types and no missing values (n = 1360076):

• status – Factor variable showing the listing status (e.g., for_sale, sold).

• price – Numeric value for the property’s price in USD.

• bed, bath – Integer counts of bedrooms and bathrooms.

• acre_lot – Numeric size of the lot (in acres).

• city, state – Factor variables identifying the property’s location.

• house_size – Numeric size of the house (in square feet).

• price_per_sqm – Numeric variable derived from price / house_size to compare prices across properties.

All rows with missing data were removed, and categorical variables were converted to factors for easier analysis and visualization later on.

Data dictionary

tibble(
  Variable = c("price", "status", "acre_lot", "state", "house_size"),
  Description = c(
    "The price for which the item was listed on the market",
    "The status if the house is already sold or still for sale",
    "The size of the land / lot on which the house is located in acres",
    "The state in which the house is located",
    "The size of the house in square feet"
  )
) |>
  kable(
    caption = "Description of key variables in the dataset",
    align = c("l", "l")
  )

Description of key variables in the dataset

Variable	Description
price	The price for which the item was listed on the market
status	The status if the house is already sold or still for sale
acre_lot	The size of the land / lot on which the house is located in acres
state	The state in which the house is located
house_size	The size of the house in square feet

Summary statistic tables

In this section we will cover the summary of our cleaned dataset. We will explore basic statistical values from our data.

Numeric Statistics

Summary of numerical values

data |> 
  janitor::clean_names() |>
  mutate(row = row_number() |> factor()) |> 
  pivot_longer(cols = where(is.numeric)) |> 
  group_by(name) |> 
  summarize(N = n(),
            min = min(value),
            mean = mean(value),
            median = median(value),
            max = max(value),
            st.dev = sd(value)
            ) |> 
  knitr::kable(digits = 2)

Summary statistics of numerical variables in dataframe

name	N	min	mean	median	max	st.dev
acre_lot	1360076	0.00	12.75	0.21	100000.0	799.83
bath	1360076	1.00	2.54	2.00	222.0	1.36
bed	1360076	1.00	3.39	3.00	444.0	1.43
house_size	1360076	100.00	2119.39	1812.00	1560780.0	4069.66
price	1360076	10400.00	573730.62	379000.00	515000000.0	1226596.98
price_per_sqm	1360076	0.02	262.42	197.42	491412.2	500.86

Interpretation

price: Very wide range ($10.4k–$51.5M). Mean ($573k), median ($379k), indicating strong right-skew and high-priced outliers.

house_size: Average ~2,119 sqft, median 1,812 sqft. Extremely large max (1,560,780 sqft) signals outliers. The distribution is right-skewed.

acre_lot: Median 0.21 acres vs. mean 12.75 acres → a few very large parcels inflate the mean.

bed / bath: Typical homes (~3 beds, 2 baths) with modest spread; minima at 1 suggest realistic counts.

price_per_sqm: Mean $262.42 vs. median $197.42, also right-skewed, consistent with price outliers.

Visualisation of numerical values

data |>
  clean_names() |>
  pivot_longer(cols = where(is.numeric)) |>
  ggplot(aes(x = value, fill = name)) +
  geom_histogram(bins = 30, alpha = 0.7, color = "white") +
  scale_x_log10(labels = label_comma()) +   # 👈 echte Werte, log-Skala
  facet_wrap(~ name, scales = "free_x") +
  theme_minimal() +
  labs(
    title = "Distribution of Numerical Variables (logarithmic scale)",
    x = "Value",
    y = "Count"
  ) +
  theme(
    legend.position = "none",
    axis.text.x = element_text(angle = 25, hjust = 1) 
    )

Nominal Statistics

Summary of nominal variables (top categories)

top_n_per_var <- 10 

nominal_summary <- data |>
  clean_names() |>
  select(where(is.factor), price) |>
  pivot_longer(cols = where(is.factor),
               names_to = "Variable",
               values_to = "Category") |>
  group_by(Variable, Category) |>
  summarise(
    Count = n(),
    Percent = round(100 * Count / nrow(data), 2),
    Mean_Price = round(mean(price, na.rm = TRUE), 0),
    .groups = "drop"
  ) |>
  group_by(Variable) |>
  slice_max(order_by = Count, n = top_n_per_var, with_ties = FALSE) |>
  ungroup()

kable(
  nominal_summary,
  caption = paste0(
    "Top ", top_n_per_var,
    " categories per factor variable (counts, share %, and mean price)"
  ),
  digits = 2,
  align = c("l", "l", "r", "r", "r")
)

Top categories for factor variables with counts, proportions, and mean price

Variable	Category	Count	Percent	Mean_Price
city	Houston	19226	1.41	477651
city	Tucson	7876	0.58	384816
city	Phoenix	7694	0.57	543665
city	Los Angeles	7556	0.56	1885626
city	Dallas	7510	0.55	587276
city	Philadelphia	7336	0.54	338467
city	Richmond	6592	0.48	392538
city	Orlando	6281	0.46	418841
city	Fort Worth	6171	0.45	389780
city	Saint Louis	5970	0.44	250029
state	California	170954	12.57	1095518
state	Texas	145394	10.69	451253
state	Florida	127675	9.39	649826
state	Arizona	54488	4.01	552916
state	Pennsylvania	51922	3.82	343792
state	New York	50935	3.75	669257
state	Georgia	49234	3.62	422988
state	Illinois	46901	3.45	357316
state	Washington	46450	3.42	728113
state	Virginia	44236	3.25	547994
status	for_sale	750493	55.18	621383
status	sold	609583	44.82	515063

Interpretation

city: Most listings in Houston, Tucson, Phoenix, and Los Angeles. Prices range widely — highest in Los Angeles (~$1.9M), lowest around $250k (Saint Louis).

state: California, Texas, and Florida dominate listings (>30% total). California shows the highest mean price (~$1.1M).

status: 55% for sale, 45% sold. Active listings are priced higher (~$621k vs. $515k).

Overall: Listings cluster in major U.S. cities and states, with strong regional price differences, especially high in California and large metro areas.

Visualisation of nominal variables (top categories)

nominal_summary <- nominal_summary |>
  group_by(Variable) |>
  mutate(Category = forcats::fct_reorder(Category, Count),
         Category = factor(Category, levels = unique(Category))) |>
  ungroup()

# Plot: Facets untereinander, mit eigener x-Skala und y-Skala pro Variable
ggplot(nominal_summary, aes(x = Count, y = Category, fill = Variable)) +
  geom_col(show.legend = FALSE, alpha = 0.8, width = 0.7) +
  facet_wrap(~ Variable, ncol = 1, scales = "free", drop = TRUE) +
  scale_x_continuous(labels = label_comma()) +   #Tausendertrennung, keine 1e+05
  theme_minimal() +
  labs(
    title = "Top Categories per Factor Variable",
    x = "Count",
    y = "Category"
  ) +
  theme(
    panel.spacing.y = unit(1, "lines"),
    strip.text = element_text(size = 12, face = "bold"),
    axis.text.y = element_text(size = 8),
    plot.margin = margin(5, 15, 5, 5)
  )

Bivariate Analysis

Pairs Plot (all numeric variables)

set.seed(123)

data_num <- data |>
  janitor::clean_names() |>
  select(where(is.numeric)) |>
  slice_sample(n = 3000) |>
  mutate(across(everything(), log1p)) 

p <- ggpairs(
  data_num,
  progress = FALSE,
  upper = list(continuous = wrap("cor", size = 4, alignPercent = 0.8, stars = TRUE)),
  lower = list(continuous = wrap("points", alpha = 0.3, size = 0.7)),
  diag = list(continuous = wrap("densityDiag", alpha = 0.7))
)

p +
  theme_minimal(base_size = 11) +
  theme(
    strip.text = element_text(size = 8, face = "bold"),
    panel.grid = element_blank(),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 9),
    plot.title = element_text(face = "bold", size = 14, hjust = 0.5)
  ) +
  labs(title = "Pairs Plot (log-transformiert, n=3000)")

Price vs. House Size by Status

# Stichprobe ziehen für Performance
set.seed(123)
sample_data <- data %>% sample_n(50000)

plot_ly(
  sample_data,
  x = ~house_size,
  y = ~price,
  color = ~status,
  type = "scatter",
  mode = "markers",
  alpha = 0.6
) %>%
  plotly::layout(
    title = list(text = "Relationship Between House Size and Price by Status"),
    xaxis = list(title = "House Size (sqft)"),
    yaxis = list(title = "Price ($)", type = "log")
  )

Interpretation

Positive relationship: Larger houses generally have higher prices, though the relationship weakens for very large properties.

Status comparison: Both for_sale and sold homes follow similar trends, but for_sale listings appear higher in price, suggesting sellers may list above sale values.

High variation: At similar sizes, prices vary widely — showing the strong influence of location and other factors.

Outliers: A few extremely large or expensive properties stretch the scale upward.

Multiple Regression

model <- lm(price ~ house_size + bath + bed + state + status, data = data)
tidy(model) |>
  arrange(p.value) |>
  mutate(
    estimate = round(estimate, 1),
    std.error = round(std.error, 1),
    statistic = round(statistic, 1),
    p.value = signif(p.value, 3)
  ) |>
  datatable(
    caption = "Regressionsergebnisse (interaktiv)",
    filter = "top",         
    options = list(
      pageLength = 10,      
      autoWidth = TRUE,
      responsive = TRUE
    )
  )

tidy(model, conf.int = TRUE) |>
  filter(term != "(Intercept)") |>
  mutate(term = reorder(term, estimate)) |>
  ggplot(aes(x = estimate, y = term, fill = estimate > 0)) +
  geom_col(show.legend = FALSE) +
  geom_vline(xintercept = 0, linetype = "dashed") +
  theme_minimal() +
  labs(
    title = "Greatest contributors to price",
    x = "regression coefficient",
    y = ""
  )

Average Property Price Map

valid_states <- tibble(
  state_name = c(state.name, "District of Columbia"),
  state_abbr = c(state.abb,  "DC")
)

map_price <- data |>
  group_by(state) |>
  summarise(avg_price = mean(price, na.rm = TRUE), .groups = "drop") |>
  inner_join(valid_states, by = c("state" = "state_name")) |>
  mutate(avg_price_k = avg_price / 1000)

plot_ly(
  map_price,
  type = "choropleth",
  locationmode = "USA-states",
  locations = ~state_abbr,
  z = ~avg_price_k,
  text = ~paste0(state, "<br>Avg Price: $", round(avg_price_k, 1), "K"),
  colorscale = list(c(0, 1), c("lightblue", "darkblue")),
  colorbar = list(title = "Avg Price ($K)")
) |>
  plotly::layout(
    title = list(text = "Average Property Price by U.S. State"),
    geo = list(scope = "usa", projection = list(type = "albers usa"))
  )

Interpretation

Regional variation: Western and coastal states show generally higher property prices, while central regions are lower.

Highest averages: States like California, New York, and Washington stand out with mean prices well above $1M.

Moderate prices: States such as Texas, Florida, and Arizona fall in the mid-range (~$400–650K).

Lower averages: Midwest and Southern states have more affordable properties on average.

Summary: Property values are heavily influenced by geography — with the highest prices concentrated along the coasts and major urban centers.

Average House Size Map

map_size <- data %>%
  group_by(state) %>%
  summarise(avg_size = mean(house_size, na.rm = TRUE), .groups = "drop") %>%
  inner_join(valid_states, by = c("state" = "state_name"))

plot_ly(
  map_size,
  type = "choropleth",
  locationmode = "USA-states",
  locations = ~state_abbr,
  z = ~avg_size,
  text = ~paste0(state, "<br>Avg Size: ", round(avg_size), " sqft"),
  colorscale = list(c(0, 1), c("lightgreen", "darkgreen")),
  colorbar = list(title = "Avg Size (sqft)")
) %>%
  plotly::layout(
    title = list(text = "Average House Size by U.S. State"),
    geo = list(scope = "usa", projection = list(type = "albers usa"))
  )

Interpretation

General trend: Average house sizes are fairly consistent across most states, typically around 2,000–2,500 sqft.

Larger homes: Some central and mountain states (e.g., Colorado, Utah, Iowa) show slightly larger averages, possibly due to more available land.

Smaller homes: Coastal and densely populated states (e.g., New York, California) tend to have smaller average house sizes.

Price Range by US State

map_extremes <- data |>
  group_by(state) |>
  summarise(
    min_price = suppressWarnings(min(price, na.rm = TRUE)),
    max_price = suppressWarnings(max(price, na.rm = TRUE)),
    .groups = "drop"
  ) |>
  mutate(range_price = max_price - min_price) |>
  inner_join(valid_states, by = c("state" = "state_name"))

plot_ly(
  map_extremes,
  type = "choropleth",
  locationmode = "USA-states",
  locations = ~state_abbr,
  z = ~range_price,
  text = ~paste0(
    state,
    "<br>Min: $", formatC(min_price, big.mark = ",", format = "f", digits = 0),
    "<br>Max: $", formatC(max_price, big.mark = ",", format = "f", digits = 0)
  ),
  colorscale = "Reds",
  colorbar = list(title = "Price Range ($)")
) |>
  plotly::layout(
    title = list(text = "Price Extrem Values by U.S. State (Max − Min)"),
    geo = list(scope = "usa", projection = list(type = "albers usa"))
  )

Interpretation

Highest ranges: California shows by far the largest price range (over $400M), driven by extremely high luxury property values.

Moderate ranges: States like Florida and parts of the Northeast also show wide price spreads, reflecting diverse markets from affordable to luxury homes.

Lower ranges: Most central and midwestern states have smaller price gaps, indicating more uniform housing markets.

Summary